An observational pilot study: Prevalence and cost of high frequency emergency department users at Örebro University Hospital, Sweden

Background There is little research on high frequency emergency department users (HEDU) in Sweden. We aim to determine the prevalence and costs of HEDU compared to non-HEDU at Örebro University Hospital (ÖUH). Additionally, we will determine the factors and outcomes associated with being a HEDU. Methods This was a retrospective, observational cohort study of ED patients presenting to ÖUH, Sweden between 2018–19. Analyses used electronic registry, ambulance, and cost data. The definition for HEDU was ≥4 visits/year. HEDUs were categorized further into Repeat, High and Super HEDU with 4–7, 8–18 and ≥19 visits/year, respectively. We used multivariable logistic regression to determine the adjusted odds ratios for factors and outcomes between HEDU and non-HEDU. Findings Of all ÖUH ED patients, 6.1% were HEDU and accounted for 22.4% of ED visits and associated costs. Compared to the mean cost of non-HEDU, the Repeat, High and Super HEDU were more costly by factors of 4, 8 and 27, respectively. The HEDUs were more likely to be male, self-referred, present with abdominal pain, arrive by ambulance, at night and from the Örebro municipal region. Super HEDU were more likely to be of adult age and assigned lower acuity scores. HEDU were more likely to be directed to the surgical zone, less likely to receive radiologic imaging or achieve a 4-hr time target. In contrast to the Repeat and High HEDU, Super HEDU were less likely to be admitted, but more likely to leave without being seen. Conclusion ÖUH has a HEDU population with associated factors and outcomes. They account for a substantial proportion of ED costs compared to non-HEDU.

Introduction ceiling to the annual cost of health care for each citizen equivalent to 1150 SEK (140.11 USD) [13]. Ö rebro is the sixth largest city in Sweden with a population of 124,027 [14], catchment area of 302 252 (2019) [15] and 60 general practitioners per 100 000 population. Ö UH is an academic hospital. Its adult-pediatric ED receives >60,000 visits per year and is a trauma, interventional cardiology, stroke, thoracic and neurosurgical center. Upon arrival, a pre-triage nurse records the patient's chief complaint and performs a quick assessment. If the chief complaint is a psychiatric, gynecological, eye or ear-nose-and-throat, the patient is re-directed to a separate specialty-specific department outside the ED. Minor orthopedic injuries are also redirected to a separate department during office hours. The main triage assessment is conducted by a registered nurse using the Swedish acuity score: Rapid Emergency Triage and Treatment System (RETTS). Once registered in the ED, patients are triaged into different ED zones, such as surgical, medical, or pediatric (<18 years old) and can, if needed, be transferred from one zone to another. Each zone transfer is considered a separate ED registration. ED staffing of doctors is predominantly rotating junior physicians representing the different specialties, as well as a smaller number of emergency consultants. Nurses have the capacity to independently assess and disposition patients. The ED has no short-stay unit. The hospital had on average 402 beds during the study period.

Patient population, data sources and objectives
The patient population was ED patients visiting Ö UH between January 1, 2018, to December 31, 2019. We used three electronic data sources: the Ö rebro ED registry, ambulance data and regional costing ledger. The Ö rebro ED Registry collected patient demographics, presenting complaint, acuity score, allocated ED zone (eg. medical, pediatrics, surgical), number of transfers between zones, provider type, disposition, and time markers (eg. discharge time). Ambulance data recorded patient information, including time of ambulance arrival. We used patient identifiers and ED encounter numbers to combine the Ö rebro ED Registry, costing ledger and ambulance data into a single dataset. The costing ledger included costs associated with the ED visit (eg. provider, radiology, or drug costs). In Sweden, there is a national standard for calculating the cost per patient. The base cost is determined by the type of personnel (doctor or nurse) caring for the patient. If the patient receives further resources, such as radiology, pathology, or laboratory, they are added to the base cost. Ambulance costs are not included. All assigned costs are validated by the Ö rebro economic department before they become official nationally. Research ethics was approved by the Swedish Ethical Review Authority (Registration number: 2020-01614).
The primary objective was the prevalence of Ö UH HEDU and associated total and mean costs per visit and patient between January 1, 2018, and December 31, 2019. Secondary objectives were factors and outcomes associated with HEDU visits from the electronic health record.

Data analysis
Because zone transfers were registered separately, only index ED registrations were counted. Duplications and registrations with missing cost data were excluded. Following international literature [5], we defined HEDU as �4 ED visits per year. Using methods by Doupe et al. [16], we calculated visit frequency by counting backwards from the patient's last ED visit within the preceding year (Fig 1). Referencing Liu et al. [17], we defined Repeat, High and Super HEDU as 4-7, 8-18 and �19 visits over one year. We collected demographics, acuity score, municipality code, referral source, arrival mode, arrival time, presenting complaint, assigned zone, number of zone transfers per visit, radiology consumption, disposition, and ED length-of-stay (LOS) by HEDU type. Using the standard age structure [18], we organized age into three groups: children (0-14yo), adults (15-64yo) and elderly (�65 yo). RETTS is a 5-tiered triage system, where the highest acuity level is red, which we have depicted as 1 [19]. For simplicity and consistency with emergency medicine literature, we combined RETTS into two categories: High (1-2) and Low (3)(4)(5) acuity. We categorized patients by disposition: admission, discharge, death or left-without-being-seen. Discharged patients were grouped into low and high acuity. EDLOS was the duration between ED arrival and discharge (i.e. door to door time). Next, organized by HEDU type and from a public healthcare payer perspective, we determined the total and mean cost by visit and patient. Then, we determined patient factors associated with HEDU type. Finally, we determined the relationship between HEDU type and outcomes of assigned zone, radiology consumption, admission and EDLOS time targets. Although the 4-hr rule has become obsolete in Sweden, other jurisdictions, such as the United Kingdom, continue to use the rule. Consequently, we applied the United Kingdom EDLOS 4-hr target [20].
We presented descriptive metrics by percentages and means with 95% percent confidence intervals. For costs, we converted Swedish krona to USD based on the January 1, 2018, conversion rate. At that time, 1 SEK was 0.121833 USD (1 USD = 8.207955 SEK) [12]. Radiology and provider costs were main contributors to total costs, so all three costs were reported. For the binary outcome of HEDU type vs. non-HEDU, we used multivariable logistic regression (alpha level of 0.05) to determine the adjusted odds ratios and 95% confidence intervals for predictor factors. Because there were three types of HEDU (Repeat, High, Super), we performed three separate regressions. For selected outcomes, we also used multivariable logistic regression (alpha level of 0.05) to determine odds ratios and 95% confidence intervals for the covariate of HEDU type (Repeat, High, Super). We used Matlab [21] to combine the Ö rebro ED Registry and ambulance data. Analyses were performed using Stata 15 [22] and Excel [23].
Mean age was 41.4 with 23.9% and 27.9% of 121,403 visits in the children and elderly group, respectively. We found that 50.9% of patients were male, and 21.5% presented with an acuity score of RETTS1-2. Of all the visits, 91.8% were self-referred, 19.0% arrived by

PLOS ONE
Cost of high frequency ED users at Ö rebro University Hospital, Sweden ambulance and 65.2% from Ö rebro municipal region. The percentage of visits during day, evening and night hours were 45.3%, 42.3% and 12.5%, respectively, with the most common presenting complaint of abdominal pain, followed by chest pain and fever. Once in the ED, 28.6%, 28.1%, 15.9%, 11.2% and 5.1% were assigned to the surgical, medical, pediatrics, orthopedics, and neurological zones, respectively. Less than 4% of patients were transferred internally, with little difference (0.3%) between HEDU and non-HEDU. About 21.7% received radiological testing. For disposition, 26.5% were admitted, 70.8% discharged, 2.7% left without being seen and 0.1% died. Median EDLOS was 3.1 hrs with the shortest being 0.4hrs (death) and longest at 3.4hrs (admissions and discharged high acuity). Although 6.1% of ED patients were HEDU, they accounted for 22.4% of ED visits. Of all the ED patients, 5.3%, 0.7% and 0.1% were Repeat (4-7 visits), High (8-18 visits) and Super (> = 19 visits) HEDU, respectively. The Super HEDU group did not have any children but did have the greatest proportion of adults (79.0%). In contrast, the Repeat HEDU group had the greatest proportion of children (24.4%) and elderly (38.0%) ( Table 1)

PLOS ONE
Cost of high frequency ED users at Ö rebro University Hospital, Sweden The Repeat, High and Super HEDU visits were more likely to be from males (OR 1.1, 1.1,  1.3, respectively), self-referred (OR 1.5, 2.7, 5.2), present with abdominal pain (OR 1.3, 1.3,  4.0), arrive by ambulance (OR 1.1, 1.2, 2.2), during the night (OR 1.1, 1.2, 1.4) and from the  (Table 3). Repeat, High and Super HEDU visits were more likely to be assigned to the surgical zone (OR 1.1, 1.2, 3.2, respectively). The visit was less likely to receive a radiologic test (OR 0.7, 0.6, 0.4) or achieve the 4-hr time target (OR 0.8, 0.8, 0.6). Repeat and High HEDU visits were more likely to be admitted (OR 1.4, 1.4, respectively). Super HEDU visits were less likely to be admitted (OR 0.9) but more likely to leave without being seen (OR 3.3) (Table 4).

Discussion
There were HEDUs at Ö UH. A small number of ED patients accounted for a significant proportion of visits and costs. Provider costs were the main contributor of total costs. Although HEDUs accounted for a smaller proportion of total radiology costs and had a lower radiology cost per visit, the Super HEDU radiology cost per patient was 16 times higher than the non-HEDU patient. The reason why is that a small number of Super HEDU patients would receive  a disproportionate number of radiographic investigations which would increase the mean radiology cost per patient. The Super HEDUs were different from the other HEDU types because they arrived by ambulance despite not being elderly, had high left-without-being-seen rates, were assigned lower acuity scores, received less radiology, were self-referred, arrived during evening-night hours and had longer ED length-of-stays. This suggests that the Super HEDU's perceived urgency for seeking emergency care was greater than what which was determined in the ED. This suggests that the ED assessment did not address the Super HEDU's needs which may have led to more visits.
According to a systematic review, HEDUs are about 5% of the ED population, but account for 21-28% of ED visits and associated costs. The review found that HEDU were more likely to be elderly, female and have a mental health diagnosis [5]. At Ö UH, we found similar HEDU proportions and higher prevalence in the elderly, but there were differences. Ö UH HEDU were more likely to be male and the Super HEDU were more likely to be of adult age. American, English and Canadian studies have also found higher odds of HEDU amongst elderly patients with mental or chronic illness [5,8,24,25]. We did not find the mental health characteristics for Ö UH HEDUs. An explanation is that Ö UH psychiatric patients attend a different ED. Consequently, HEDU findings from other studies may not be useful to Ö UH if it decides to design interventions tailored to its population [4].
In Ontario, Canada, 5% of high-cost users account for 61% of home care and hospital costs [26]. Because Ö UH HEDUs were more likely to be admitted, costs were likely underestimated. Furthermore, like other studies [24,25,27], we have found Ö UH HEDUs were more likely to arrive by ambulance. Since we did not collect ambulance costs, we underestimated the HEDU cost.
Our pilot study was based on a high-quality administrative database with minimal missing data and duplicates. Selection bias was unlikely since we did not extract a data sample but included all electronic registrations after exclusions were removed. The linking of Swedish administrative data is reproducible and efficient by using a unique personal identification number and ED encounter number. Since there is a national standard for costing with validation by the Ö rebro economic department, cost data are highly reliable. There were, however, limitations. We only studied a single hospital, limiting generalizability; however, this was a pilot feasibility study. On the other hand, our case costing methods can be used broadly. There was information bias. We found the electronic health record data could have been more comprehensive. Diagnostic labels were basic and did not provide enough detail for a fulsome analysis. Consequently, we only used the presenting complaint. There was no record of mental health or substance abuse in Ö UH administrative databases, although found in other Swedish studies [7]. The re-direction of psychiatric presentations to another ED was a contributing factor, but we suspect that Ö UH encountered these presentations without being recorded.
To further study HEDU in Sweden, administrative databases could be made more comprehensive. Not only could more diagnostic data be collected, but also socioeconomic [28], resource, community care, admission, costs and qualitative data [11] too. Although individual hospital HEDU populations are not necessarily similar, they could share data to create regional or national databases. Finding common HEDU characteristics could help develop prediction tools [29,30]. With readily available cost data, it is easier to perform economic analyses.
We suggest that HEDUs could be a health system performance indicator of coordinated care. The reason why is that interventions to decrease HEDU visits are coordinated care models, such as case management, individualized care plans and information sharing [31]. According to the OECD, Sweden's healthcare system is highly integrated [32] but struggles to provide better care coordination for those with chronic diseases and access to primary care [33], so perhaps the HEDU population could be a marker of coordination. The Institute of Healthcare Improvement's "Triple Aim" for populations is: "1) Improving the patient experience of care 2) Improving the health of populations and 3) Reducing the per capita cost of health care. [34]" We suggest that HEDU costs be considered as a quality improvement measure for any healthcare system trying to achieve the Triple Aim [35]. In Ontario, Canada, Health Quality Ontario uses quality improvement with coordinated care plans, called HealthLinks, to improve the care for the HEDU and high-cost user population [36,37]. A 2015 systematic review found the impact of coordinated care plans, information sharing and case management to be modest but not ineffective [31].
Given the unique features of the Ö UH HEDU population, a local, tailored strategy is required. Consequently, a quality improvement plan would be a suitable approach [38]. Because of Ö UH's large number of rotating physicians, it will be challenging to identify HEDUs through them. The electronic health record [39] and social workers [28] are potential identification strategies. For more effective community care, community health services, emergency department, paramedics, medical and surgical services could work together to develop coordinated care plans [40][41][42][43]. We recommend that coverage be extended into night hours and that coordinated care plans be tailored to the individual, requiring continual review [4].

Conclusion
There is a HEDU population at Ö UH who account for a disproportionate amount of ED costs. From the electronic health record, there are factors and outcomes associated with being a HEDU that are unique to Ö rebro and not found within international literature. It is feasible to collect HEDU data from Swedish hospitals and municipal region databases.